Call for Paper - May 2015 Edition
IJCA solicits original research papers for the May 2015 Edition. Last date of manuscript submission is April 20, 2015. Read More
Resolution Improvement in Diffuse Optical Tomography
IJCA Proceedings on International Conference on Innovations In Intelligent Instrumentation, Optimization and Electrical Sciences
© 2013 by IJCA Journal
ICIIIOES - Number 9
Year of Publication: 2013
R.sukanya Devi, Uma K Maheswari and S Sathiyamoorthy. Article: Resolution Improvement in Diffuse Optical Tomography. IJCA Proceedings on International Conference on Innovations In Intelligent Instrumentation, Optimization and Electrical Sciences ICIIIOES(9):37-41, December 2013. Full text available. BibTeX
@article{key:article,
author = {R.sukanya Devi and K. Uma Maheswari and S. Sathiyamoorthy},
title = {Article: Resolution Improvement in Diffuse Optical Tomography},
journal = {IJCA Proceedings on International Conference on Innovations In Intelligent Instrumentation, Optimization and Electrical Sciences},
year = {2013},
volume = {ICIIIOES},
number = {9},
pages = {37-41},
month = {December},
note = {Full text available}
}
Abstract
Diffuse Optical Tomography (DOT) is an imaging technique which uses Near Infrared light to estimate the functional information of biological soft tissues. The recovery of internal optical parameters are illustrated using non-invasive boundary measurements. DOT involves solving an inverse problem which has an ill-condition of non linearity. To overcome this drawback regularization techniques are implemented in the inverse formulation. In this work a model based regularization technique is proposed, which uses model resolution matrix and data resolution matrix to improve the resolution of the reconstructed image. Simulations are performed by reconstructing a 1% noise data in MATLAB interfaced with NIRFAST and the results illustrates model based regularization improves the resolution of the object with better absorption coefficients.
References
- S. L. Jacques and B. W. Pogue, "Tutorial on diffuse light transport," J. Biomed. Opt. ,13, 04130, 2008.
- A. Gibson, J. C. Hebden, and S. R. Arridge, "Recent advances in diffuse optical tomography," Phys. Med. Bio. ,50, R1–R43, 2005.
- A. Gibson and H. Dehghani, "Diffuse optical imaging," Phil. Trans. R. Soc. ,A 367, 3055–3072, 2009.
- S. R. Arridge, "Optical tomography in medical imaging," Inverse Probl. , 15, R41–R93, 1999 .
- S. R. Arridge and J. C. Hebden, "Optical imaging in medicine: II. Modelling and reconstruction," Phys. Med. Biol. ,42, 841–853, 1997.
- H. Dehghani, S. Srinivasan, B. W. Pogue, and A. Gibson, "Numerical modelling and image reconstruction in diffuse optical tomography," Phil. Trans. R. Soc. ,A 367, 3073–3093, 2009.
- S. R. Arridge and M. Schweiger, "Photon- measurement density functions. Part 2:Finite-element- method calculations,"Appl. Opt. , 34, 8026–8037, 1995.
- M. Schweiger, S. R. Arridge, M. Hiroaka, and D. T. Delpy, "The finite element model for the propagation of light in scattering media: Boundary and source conditions," Med. Phys. , 22, 1779–1792, 1995.
- M. Guven, B. Yazici, X. Intes, and B. Chance, "Diffuse optical tomography with a priori anatomical information," Phys. Med. Biol. , 50, 2837–2858, 2005.
- P. K. Yalavarthy, H. Dehghani, B. W. Pogue, and K. D. Paulsen, "Critical computational aspects of near infrared circular tomographic imaging: Analysis of measurement number, mesh resolution and reconstruction basis," Opt. Express ,14, 6113–6127, 2006 .
- H. Niu, P. Guo, L. Ji, Q. Zhao, and T. Jiang, "Improving image quality of diffuse optical tomography with a projection-errorbased adaptive regularization method," Opt. Express,16, 12423–12434, 2008.
- P. K. Yalavarthy, B. W. Pogue, H. Dehghani, and K. . D. Paulsen, "Weight-matrix structured regularization provides optimal generalized least-squares estimate in diffuse optical tomography," Med. Phys. , 34, 2085–2098, 2007.
- P. K. Yalavarthy, B. W. Pogue, H. Dehghani, C. M. Carpenter, S. Jiang, and K. D. Paulsen, "Structural information within regularization matrices improves near infrared diffuse optical tomography," Opt. Express,15, 8043–8058, 2007.
- F. Larusson,, S. Fantini,and E. L Miller, "Hyperspectral image reconstruction for diffuse optical tomography," Biomed. Opt. Express,2, 946–965, 2011.
- H. Dehghani, M. E. Eames, P. K. Yalavarthy, S. C. Davis, S. Srinivasan, C. M. Carpenter, B. W. Pogue, and K. D. Paulsen,"Near infrared optical tomography using NIRFAST: Algorithms for numerical model and image reconstruction algorithms," Commun. Numer. Methods Eng. ,25, 711–732, 2009 .
- S. H. Katamreddy , P. K. Yalavarthy,"Model-resolution based regularization improves near infrared diffuse optical tomography", J. Opt. Soc. Am. A ,Vol. 29, No. 5, May 2012.
- K. Koh, S. Kim, S. Boyd l1 ls "A Matlab solver for large-Scale l1-Regularized Least Squares Problems", May 15, 2008
- P. K. Yalavarthy, H. Dehghani, K. D. Paulsen, "Implementation of a computationally efficient least-squares algorithm for highly under-determined three-dimensional diffuse optical tomography problems" Medical Physics, Vol. 35, No. 5, May 2008.
- Y. Zhai1 and Steven A Cummer,"Fast tomographic reconstruction strategy for diffuse optical tomography",Optics express 5285,Vol. 17, No. 7,30 March 2009.
- K. K. -Hsin Wang and T. C. Zhu," Reconstruction of optical properties using a diffusion model for interstitial diffuse optical tomography", Proc. of SPIE, Vol. 7164,2009.
- M. E. Eames, B. W. Pogue, P. K. Yalavarthy and H. Dehghani, "An efficient Jacobian reduction method for diffuse optical image reconstruction" Optics express 15909, 26 Vol. 15, No. 24, November 2007.
- S. Oraintara, William C. Karl, D. A. Castanon, T. Q. Nguyen,"A method for choosing the regularization parameter in generalized tikhonov regularized linear inverse problems", Boston university.